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Sökning: WFRF:(Lee SH)

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  • Jeong, H, et al. (författare)
  • Inhibition of C1-Ten PTPase activity reduces insulin resistance through IRS-1 and AMPK pathways
  • 2017
  • Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 7:1, s. 17777-
  • Tidskriftsartikel (refereegranskat)abstract
    • Insulin resistance causes type 2 diabetes; therefore, increasing insulin sensitivity is a therapeutic approach against type 2 diabetes. Activating AMP-activated protein kinase (AMPK) is an effective approach for treating diabetes, and reduced insulin receptor substrate-1 (IRS-1) protein levels have been suggested as a molecular mechanism causing insulin resistance. Thus, dual targeting of AMPK and IRS-1 might provide an ideal way to treat diabetes. We found that 15,16-dihydrotanshinone I (DHTS), as a C1-Ten protein tyrosine phosphatase inhibitor, increased IRS-1 stability, improved glucose tolerance and reduced muscle atrophy. Identification of DHTS as a C1-Ten inhibitor revealed a new function of C1-Ten in AMPK inhibition, possibly through regulation of IRS-1. These findings suggest that C1-Ten inhibition by DHTS could provide a novel therapeutic strategy for insulin resistance-associated metabolic syndrome through dual targeting of IRS-1 and AMPK.
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  • Kang, JS, et al. (författare)
  • Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning
  • 2020
  • Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1, s. 20140-
  • Tidskriftsartikel (refereegranskat)abstract
    • Most models for predicting malignant pancreatic intraductal papillary mucinous neoplasms were developed based on logistic regression (LR) analysis. Our study aimed to develop risk prediction models using machine learning (ML) and LR techniques and compare their performances. This was a multinational, multi-institutional, retrospective study. Clinical variables including age, sex, main duct diameter, cyst size, mural nodule, and tumour location were factors considered for model development (MD). After the division into a MD set and a test set (2:1), the best ML and LR models were developed by training with the MD set using a tenfold cross validation. The test area under the receiver operating curves (AUCs) of the two models were calculated using an independent test set. A total of 3,708 patients were included. The stacked ensemble algorithm in the ML model and variable combinations containing all variables in the LR model were the most chosen during 200 repetitions. After 200 repetitions, the mean AUCs of the ML and LR models were comparable (0.725 vs. 0.725). The performances of the ML and LR models were comparable. The LR model was more practical than ML counterpart, because of its convenience in clinical use and simple interpretability.
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